Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study

Abstract

Background Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance. Methods The AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden’s index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05). Results Sensitivity, specificity, and Youden’s index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976–0.983, impacted teeth), 0.975 (95%CI: 0.972–0.978, full crowns), and 0.935 (95%CI: 0.929–0.940, residual roots), 0.939 (95%CI: 0.934–0.944, missing teeth), and 0.772 (95%CI: 0.764–0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001). Conclusions The AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3–10 years of experience. However, the AI framework for caries diagnosis should be improved.

Periapical Lesions in Panoramic Radiography and CBCT Imaging—Assessment of AI’s Diagnostic Accuracy

“…Our study supports the thesis of high AI performance in CBCT evaluation. The reported sensitivity of PL detection is in line with that of other studies [31,[50][51][52][53]. A study by Orhan et al [31] showed that Diagnocat achieved 92.8% accuracy in PL detection on CBCT images.…”

Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies

“…Within this subset, 67 literature reviews, 46 editorials, and 37 seminar articles were excluded, aligning with the study's focus on primary research articles. This comprehensive curation and scrutiny culminated in the inclusion of nine in-vitro papers [ [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] ] in the review, which met the specified eligibility criteria. Table 3 presents the overview of the included in-vitro papers [ [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] ].…”

“… Verhelst et al [ 34 ] 160 CBCT scans Automatic creation of 3D surface models of the human mandible from CBCT images Layered deep learning algorithm Anonymized full skull CBCT scans Time for segmentation: 17s, IoU: 94.6 %, DSC: 94.4 %, HD: Not specified AI exhibited significantly faster mandible surface model creation with comparable accuracy. Zhu et al [ 35 ] 2278 scans Diagnosis of multiple dental diseases on panoramic radiographs (PRs) BDU-Net, nnU-Net Not specified Sensitivity, Specificity, AUC: Vary by disease, Diagnostic time: Shorter than dentists AI demonstrated comparable or better diagnostic performance in multiple dental disease diagnoses. …”

“…AI exhibited significantly faster mandible surface model creation with comparable accuracy, reducing the segmentation time considerably. Zhu et al [ 35 ] investigated the diagnosis of multiple dental diseases on PRs using BDU-Net and nnU-Net models. Their AI demonstrated comparable or superior diagnostic performance to human dentists in various diseases, while also significantly reducing diagnostic time [ [38] , [39] , [40] , [41] , [42] ].…”

Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making

“…Mima et al introduced a method utilizing Faster R-CNNs for detecting and classifying tooth regions in dental panoramic X-ray images with high precision and accuracy [ 26 ]. Zhu et al introduced an AI framework for diagnosing common dental diseases from panoramic radiographs, showing high specificity for all diseases except for caries, where it showed lower sensitivity and specificity, but overall, it demonstrated superior diagnostic performance compared to dentists in certain areas [ 27 ].…”